In the design and production of multi-layer microelectronic structures, such as semiconductor computer chips and the like, it is of great importance to map out the location of electrical short circuits in computer chips and multi-chip modules. The electrical short circuits occur when wires in the chip or in a multi-chip module are shorted to each other, to a ground plane, or to a power plane.
When a computer chip is “on”, currents flow through wires in the chip. Only certain wires should be electrically connected together for a given chip to operate in a prescribed manner. However, many chips fail because of connections between wires that are not supposed to be connected. Problems may arise either on the chip, or in the module that the chip resides. If the precise location of such a fault can be accurately determined, then a manufacturer can change the design of the chip or correct the fabrication process to reduce such defects, improve the yield, and increase profits.
At present, it is difficult to precisely locate faults using conventional techniques since most chips are “flip chip” bonded onto a carrier or multi-chip module so that the circuit itself is neither visible nor accessible. A similar problem exists for the detection of short circuits in multi-chip modules wherein the modules may have many layers and may be up to a millimeter thick. The tools that the semiconductor industry would ordinarily use to find faults are practically useless in this type of system. Additionally, sophisticated techniques, such as the use of an infrared camera to detect local heating from a short in a module, yields relatively poor spatial resolution since the modules are too thick and the fact that heat diffuses over a relatively large area.
Conventional evaluation of chips is expensive in both time and resources, and often unfeasible as well as destructive. One method for non-destructive evaluation of the problems occurring in semiconductor chips or modules is running current through the chip and then imaging the magnetic field produced at the surface.
The magnetic field image can then be inverted by a well-known algorithm into a current density map, which subsequently can be compared to CAD diagrams of the chip wiring around the problem area and the location of the problem may be determined. Scanning SQUID (Superconducting Quantum Interference Device) microscopes can acquire such magnetic images and can be successfully used for applications in the semiconductor circuit diagnostics. The main advantage that the SQUID microscope has over all other imaging schemes is that the chips, even metallic conducting regions thereof, are completely transparent to magnetic fields.
The SQUID microscopes however possess an even more important advantage over other fault detection techniques wherein they can resolve fine details at a distance. This is important since the current carrying portions of a chip or multi-chip module are hidden on the backside of a flip chip wafer and are thus distanced from the detection microscope by 100–200 micrometers. To resolve details that are much smaller than this stand-off is exponentially difficult. However, by using Maxwell's Equations, the physical laws that govern the behavior of electric and magnetic fields, the magnetic field images acquired by the SQUID can be converted into direct images of the current flowing in the chip. The process of converting a magnetic field image into a picture of the source currents that created the field is called a “magnetic inversion”.
Any image acquired with the SQUID system is of a finite size. Beyond the edges of the image, no information about the magnetic field is acquired. When such finite size images are converted into current images by the magnetic inversion process, the edges of the image cause artifacts (noise or interference) which can obscure the wires and also prevent achieving a finer spatial resolution. Such artifacts arise since the field effectively appears to drop to zero at the edge of the image, which in fact is not the case.
Manhattan Filters (the technique developed at the University of Maryland by the Applicant of the present patent application) have been used to reduce these edge artifacts. The Manhattan Filter technique involves the manual removal of regions of the transformed image which contain the edge artifact information. In some cases this is an effective method for removing the edge artifacts. However, such a technique fails to be effective when the main features in the image are aligned with the edges of the image. An additional drawback of the Manhattan Filtering technique is that it requires an expert to recognize which regions of the transformed image should be removed.
Most methods for removing edge artifacts in images involve smoothing the border of the original image since it has the sharp edge which generates the artifacts. However, this is a generally unsatisfactory solution as it involves modifying, or even losing, valuable data. The primary concern of magnetic microscopy is obtaining the finest spatial resolution possible in the final current density image and eliminating edge effects that arise due to finite image size. It is thus highly desirable to develop a technique which effectively eliminates edge artifacts and which can be easily automated and incorporated into existing magnetic inversion software.